Results 121 to 130 of about 86,248 (260)

Increasing-Margin Adversarial (IMA) training to improve adversarial robustness of neural networks. [PDF]

Comput Methods Programs Biomed, 2023
Ma L, Liang L.
europepmc   +1 more source

On Evaluating Adversarial Robustness

, 2019
Correctly evaluating defenses against adversarial examples has proven to be extremely difficult. Despite the significant amount of recent work attempting to design defenses that withstand adaptive attacks, few have succeeded; most papers that propose defenses are quickly shown to be incorrect. We believe a large contributing factor is the difficulty of
Carlini, Nicholas, Athalye, Anish, Papernot, Nicolas, Brendel, Wieland, Rauber, Jonas, Tsipras, Dimitris, Goodfellow, Ian, Madry, Aleksander, Kurakin, Alexey   +8 more
openaire   +2 more sources

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

Advanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source

Towards Adversarial Robustness for Multi-Mode Data through Metric Learning. [PDF]

Sensors (Basel), 2023
Khan S, Chen JC, Liao WH, Chen CS.
europepmc   +1 more source

Uncertainty‐Guided Selective Adaptation Enables Cross‐Platform Predictive Fluorescence Microscopy

Advanced Intelligent Discovery, EarlyView.
Deep learning models often fail when transferred to new microscopes. A novel framework overcomes this by selectively adapting the early layers governing low‐level image statistics, while freezing deep layers that encode morphology. This uncertainty‐guided approach enables robust, label‐free virtual staining across diverse systems, democratizing ...
Kai‐Wen K. Yang, Andrew Bai, Alexandra Bermudez, Yunqi Hong, Zoe Latham, Iris Sloan, Michael Liu, Vishrut Goyal, Cho‐Jui Hsieh, Neil Y. C. Lin   +9 more
wiley   +1 more source

Adversarial robustness guarantees for quantum classifiers

npj Quantum Information
Despite their ever more widespread deployment throughout society, machine learning algorithms remain critically vulnerable to being spoofed by subtle adversarial tampering with their input data.
Neil Dowling, Maxwell T. West, Angus Southwell, Azar C. Nakhl, Martin Sevior, Muhammad Usman, Kavan Modi   +6 more
doaj   +1 more source

Improving Adversarial Robustness of Deep Neural Networks via Adaptive Margin Evolution. [PDF]

Neurocomputing (Amst), 2023
Ma L, Liang L.
europepmc   +1 more source

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source

Improving the Interpretability of fMRI Decoding using Deep Neural Networks and Adversarial Robustness. [PDF]

Apert Neuro, 2023
McClure P, Moraczewski D, Lam KC, Thomas A, Pereira F.   +4 more
europepmc   +1 more source

Robust Decision Trees Against Adversarial Examples

, 2019
Although adversarial examples and model robustness have been extensively studied in the context of linear models and neural networks, research on this issue in tree-based models and how to make tree-based models robust against adversarial examples is still limited.
Chen, H, Zhang, H, Boning, D, Hsieh, CJ
openaire   +3 more sources